Prior ESM studies on affective reactivity
Data preprocessing
Data preprocessing was conducted largely in accordance with Schoevers et al. (2020). Several checks were performed on the EMA data before using these in further statistical analyses. First, we checked whether responses exceeded the minimum or maximum score (<1 or >7) (0 observations, 0 participants). Second, exceedance of the maximum response time was checked. Respondents were instructed to fill-out the questionnaires as soon as possible after receiving the text message (beep), preferably within 15 minutes but no later than 60 minutes. The patient received a reminder after 30 minutes. We coded observations as missing if they were not uploaded within 65 minutes after the participants were invited by a text message to fill-out the questionnaire (18 observations, from 17 participants). Third, we identified observations that are missing because individuals opened the questionnaire but did not fill it in (0 observations, 0 participants) or because of technical failure (e.g., server down n=19). Fourth, participants with a response rate below 50% (35 of 70 observations) were excluded from the analyses (n = 8). Fifth, participants were excluded if they showed no variation on the positive affect or negative affect scales (1 participant).
Data preparation in R
rm(list=ls(all=T))
library(plyr)
library(ggplot2)
library(moments)
library(knitr)
library(dplyr)
library(ggpubr)
library(FSA)
library(lme4)
library(lmerTest)
library(reshape2)
library(stringr)
library(car)
library(effects)
library(DHARMa)
library(GLMMadaptive)
load("/Users/linovonklipstein/Documents/Research Data/NESDA/after preprocessing/formatted data.Rdata")
Variables in loaded dataset (emaD):
pident = participant identification number
negA = negative affect (range 1-7)
posA = positive affect (range 1-7)
posE = positive event (dichotomous; 0-“no”, 1-“yes”)
negE = negative event (dichotomous; 0-“no”, 1-“yes”)
negA_lag1 = NA at t-1 (set to missing at first measurement of a day)
posA_lag1 = PA at t-1 (set to missing at first measurement of a day)
# reorder group factor for clarity
emaD$depgroup <- factor(emaD$depgroup, levels = c("control", "remitted", "current"))
# create second group factor, where "current" is the first group (and will serve as reference group in some analyses)
emaD$depgroup2 <- factor(emaD$depgroup, levels = c("current", "remitted", "control"))
# calculate person-mean centered event variables
emaD <- emaD %>%
group_by(pident) %>%
mutate(negE.pm = mean(negE, na.rm = T),
negE.pmc = negE - negE.pm,
posE.pm = mean(posE, na.rm = T),
posE.pmc = posE - posE.pm) %>%
ungroup()
# calculate person-mean centered lagged affect variables
emaD <- emaD %>%
group_by(pident) %>%
mutate(negA_lag1.pm = mean(negA_lag1, na.rm = T),
negA_lag1.pmc = negA_lag1 - negA_lag1.pm,
posA_lag1.pm = mean(posA_lag1, na.rm = T),
posA_lag1.pmc = posA_lag1 - posA_lag1.pm) %>%
ungroup()
# substract 1 from affect variables to make minimum value 0 (necessary for two-part models, but used in all models for consistency)
emaD$negA <- emaD$negA-1
emaD$posA <- emaD$posA-1
Analysis step 1: Tests of group differences in sample characteristics
Age
# aov_age <- aov(fage ~ depgroup, data = wavesD)
# summary(aov_age)
# plot(aov_age, 1)
# plot(aov_age, 2)
# leveneTest(fage ~ depgroup, data = wavesD) # residuals not normal -> kruskal-wallis
kruskal.test(fage ~ depgroup, data = wavesD)
##
## Kruskal-Wallis rank sum test
##
## data: fage by depgroup
## Kruskal-Wallis chi-squared = 5.3245, df = 2, p-value = 0.06979
Gender
gender_table <- ddply(wavesD, "depgroup", summarise,
female_N = sum(sex == "female"),
male_N = sum(sex == "male")
)
chisq.test(gender_table[,2:3])
##
## Pearson's Chi-squared test
##
## data: gender_table[, 2:3]
## X-squared = 1.1503, df = 2, p-value = 0.5626
Years of education
aov_eduyears <- aov(fedu ~ depgroup, data = wavesD)
summary(aov_eduyears)
## Df Sum Sq Mean Sq F value Pr(>F)
## depgroup 2 95 47.50 5.181 0.00607 **
## Residuals 343 3145 9.17
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
# plot(aov_eduyears, 1)
# plot(aov_eduyears, 2)
# leveneTest(fedu ~ depgroup, data = wavesD) # variance of residuals is homogeneous and they are approximately normal -> stick with ANOVA
IDS-SR
# aov_ids <- aov(fids ~ depgroup, data = wavesD)
# summary(aov_ids)
# plot(aov_ids, 1)
# plot(aov_ids, 2)
# leveneTest(fids ~ depgroup, data = wavesD) # residuals not normal -> Kruskal-Wallis
kruskal.test(fids ~ depgroup, data = wavesD)
##
## Kruskal-Wallis rank sum test
##
## data: fids by depgroup
## Kruskal-Wallis chi-squared = 137.18, df = 2, p-value < 2.2e-16
BAI
# aov_bai <- aov(fbaiscal ~ depgroup, data = wavesD)
# summary(aov_bai)
# plot(aov_bai, 1)
# plot(aov_bai, 2)
# leveneTest(fbaiscal ~ depgroup, data = wavesD) # residuals not normal -> Kruskal-Wallis
kruskal.test(fbaiscal ~ depgroup, data = wavesD)
##
## Kruskal-Wallis rank sum test
##
## data: fbaiscal by depgroup
## Kruskal-Wallis chi-squared = 88.087, df = 2, p-value < 2.2e-16
Number of positive events
posE_byID <- ddply(emaD, "pident", summarise,
Event = sum(posE, na.rm = T),
NoEvent = sum(posE == 0, na.rm = T),
Missing = sum(is.na(posE)),
Group = depgroup[1])
# aov_posE <- aov(Event ~ Group, data = posE_byID)
# summary(aov_posE)
# plot(aov_posE, 1)
# plot(aov_posE, 2)
# leveneTest(Event ~ Group, data = posE_byID) # residuals not normal -> Kruskal-Wallis
kruskal.test(Event ~ Group, data = posE_byID)
##
## Kruskal-Wallis rank sum test
##
## data: Event by Group
## Kruskal-Wallis chi-squared = 4.4294, df = 2, p-value = 0.1092
Number of negative events
negE_byID <- ddply(emaD, "pident", summarise,
Event = sum(negE, na.rm = T),
NoEvent = sum(negE == 0, na.rm = T),
Missing = sum(is.na(negE)),
Group = depgroup[1])
# aov_negE <- aov(Event ~ Group, data = negE_byID)
# summary(aov_negE)
# plot(aov_negE, 1)
# plot(aov_negE, 2)
# leveneTest(Event ~ Group, data = negE_byID) # residuals not normal -> Kruskal-Wallis
kruskal.test(Event ~ Group, data = negE_byID)
##
## Kruskal-Wallis rank sum test
##
## data: Event by Group
## Kruskal-Wallis chi-squared = 12.757, df = 2, p-value = 0.001698
Analysis step 2: Within-person means, standard deviations, and skewness
Because these within-person statistics were not normally distributed, we used non-parametric tests to statistically compare them between groups. The Kruskal-Wallis test was used as an omnibus test for group differences and Dunn’s test was subsequently used to test for pairwise comparisons. We employed the false discovery rate to correct p-values and protect against false positive inflation.
Test code and p-value correction
withinp_descriptives <- ddply(emaD, "pident", summarise,
M_NA = mean(negA, na.rm = T),
SD_NA = sd(negA, na.rm = T),
skew_NA = skewness(negA, na.rm = T),
M_PA = mean(posA, na.rm = T),
SD_PA = sd(posA, na.rm = T),
skew_PA = skewness(posA, na.rm = T),
Group = depgroup[1]
)
kruskal_M_NA <- kruskal.test(M_NA ~ Group, data = withinp_descriptives)
kruskal_M_PA <- kruskal.test(M_PA ~ Group, data = withinp_descriptives)
kruskal_SD_NA <- kruskal.test(SD_NA ~ Group, data = withinp_descriptives)
kruskal_SD_PA <- kruskal.test(SD_PA ~ Group, data = withinp_descriptives)
kruskal_skew_NA <- kruskal.test(skew_NA ~ Group, data = withinp_descriptives)
kruskal_skew_PA <- kruskal.test(skew_PA ~ Group, data = withinp_descriptives)
dunn_M_NA <- dunnTest(M_NA ~ Group, data = withinp_descriptives, method="none")
dunn_M_PA <- dunnTest(M_PA ~ Group, data = withinp_descriptives, method="none")
dunn_SD_NA <- dunnTest(SD_NA ~ Group, data = withinp_descriptives, method="none")
dunn_SD_PA <- dunnTest(SD_PA ~ Group, data = withinp_descriptives, method="none")
dunn_skew_NA <- dunnTest(skew_NA ~ Group, data = withinp_descriptives, method="none")
dunn_skew_PA <- dunnTest(skew_PA ~ Group, data = withinp_descriptives, method="none")
p_values_tests <- data.frame(model = c("kruskal_M_NA",
"kruskal_M_PA",
"kruskal_SD_NA",
"kruskal_SD_PA",
"kruskal_skew_NA",
"kruskal_skew_PA",
rep("dunn_M_NA",3),
rep("dunn_M_PA",3),
rep("dunn_SD_NA",3),
rep("dunn_SD_PA",3),
rep("dunn_skew_NA",3),
rep("dunn_skew_PA",3)),
p = c(kruskal_M_NA$p.value,
kruskal_M_PA$p.value,
kruskal_SD_NA$p.value,
kruskal_SD_PA$p.value,
kruskal_skew_NA$p.value,
kruskal_skew_PA$p.value,
dunn_M_NA$res$P.unadj,
dunn_M_PA$res$P.unadj,
dunn_SD_NA$res$P.unadj,
dunn_SD_PA$res$P.unadj,
dunn_skew_NA$res$P.unadj,
dunn_skew_PA$res$P.unadj)
)
p_values_tests$p_adj <- p.adjust(p_values_tests$p, method = "BH")
Kruskal-Wallis omnibus tests
Within-person means
NA
##
## Kruskal-Wallis rank sum test
##
## data: M_NA by Group
## Kruskal-Wallis chi-squared = 102.49, df = 2, p-value < 2.2e-16
PA
##
## Kruskal-Wallis rank sum test
##
## data: M_PA by Group
## Kruskal-Wallis chi-squared = 96.192, df = 2, p-value < 2.2e-16
Within-person standard deviations
NA
##
## Kruskal-Wallis rank sum test
##
## data: SD_NA by Group
## Kruskal-Wallis chi-squared = 99.825, df = 2, p-value < 2.2e-16
PA
##
## Kruskal-Wallis rank sum test
##
## data: SD_PA by Group
## Kruskal-Wallis chi-squared = 43.042, df = 2, p-value = 1.038e-09
Within-person skewness
NA
##
## Kruskal-Wallis rank sum test
##
## data: skew_NA by Group
## Kruskal-Wallis chi-squared = 62.144, df = 2, p-value = 9.612e-14
PA
##
## Kruskal-Wallis rank sum test
##
## data: skew_PA by Group
## Kruskal-Wallis chi-squared = 10.494, df = 2, p-value = 0.006015
Dunn’s tests for pairwise comparisons
Within-person means
NA
## Comparison Z P.adj
## 1 control - current -10.046567 2.282970e-22
## 2 control - remitted -6.175364 1.320228e-09
## 3 current - remitted 5.915153 5.684002e-09
PA
## Comparison Z P.adj
## 1 control - current 9.734479 1.179197e-21
## 2 control - remitted 5.972300 4.318742e-09
## 3 current - remitted -5.740963 1.506238e-08
Within-person standard deviations
NA
## Comparison Z P.adj
## 1 control - current -9.720873 1.179197e-21
## 2 control - remitted -6.923434 1.175535e-11
## 3 current - remitted 4.916322 1.244967e-06
PA
## Comparison Z P.adj
## 1 control - current -6.226920 1.037879e-09
## 2 control - remitted -4.940614 1.168152e-06
## 3 current - remitted 2.718894 7.145525e-03
Within-person skewness
NA
## Comparison Z P.adj
## 1 control - current 7.832951 1.633881e-14
## 2 control - remitted 4.741061 2.834692e-06
## 3 current - remitted -4.674519 3.721809e-06
PA
## Comparison Z P.adj
## 1 control - current -3.222490 0.001524982
## 2 control - remitted -1.920830 0.054753168
## 3 current - remitted 1.948341 0.053607837
Analysis step 3: Replication analysis
Model code and p-value correction
# main effect models
Model_NA_negE <- lmer(negA ~ negE.pmc + negA_lag1.pmc + negE.pm + (negE.pmc + negA_lag1.pmc|pident) , data = emaD)
Model_NA_posE <- lmer(negA ~ posE.pmc + negA_lag1.pmc + posE.pm + (posE.pmc + negA_lag1.pmc|pident) , data = emaD)
Model_PA_negE <- lmer(posA ~ negE.pmc + posA_lag1.pmc + negE.pm + (negE.pmc + posA_lag1.pmc|pident) , data = emaD)
Model_PA_posE <- lmer(posA ~ posE.pmc + posA_lag1.pmc + posE.pm + (posE.pmc + posA_lag1.pmc|pident) , data = emaD)
# group difference models
Model_NA_negE_groups <- lmer(negA ~ negE.pmc + negA_lag1.pmc + depgroup + negE.pm + depgroup*negE.pmc + depgroup*negE.pm + (negE.pmc + negA_lag1.pmc|pident) , data = emaD, control=lmerControl(optimizer = "bobyqa", optCtrl=list(maxfun=2e6)))
Model_NA_negE_groups2 <- lmer(negA ~ negE.pmc + negA_lag1.pmc + depgroup2 + negE.pm + depgroup2*negE.pmc + depgroup2*negE.pm + (negE.pmc + negA_lag1.pmc|pident) , data = emaD, control=lmerControl(optimizer = "bobyqa", optCtrl=list(maxfun=2e6)))
Model_NA_posE_groups <- lmer(negA ~ posE.pmc + negA_lag1.pmc + depgroup + posE.pm + depgroup*posE.pmc + depgroup*posE.pm + (posE.pmc + negA_lag1.pmc|pident) , data = emaD)
Model_NA_posE_groups2 <- lmer(negA ~ posE.pmc + negA_lag1.pmc + depgroup2 + posE.pm + depgroup2*posE.pmc + depgroup2*posE.pm + (posE.pmc + negA_lag1.pmc|pident) , data = emaD)
Model_PA_negE_groups <- lmer(posA ~ negE.pmc + posA_lag1.pmc + depgroup + negE.pm + depgroup*negE.pmc + depgroup*negE.pm + (negE.pmc + posA_lag1.pmc|pident) , data = emaD)
Model_PA_negE_groups2 <- lmer(posA ~ negE.pmc + posA_lag1.pmc + depgroup2 + negE.pm + depgroup2*negE.pmc + depgroup2*negE.pm + (negE.pmc + posA_lag1.pmc|pident) , data = emaD)
Model_PA_posE_groups <- lmer(posA ~ posE.pmc + posA_lag1.pmc + depgroup + posE.pm + depgroup*posE.pmc + depgroup*posE.pm + (posE.pmc + posA_lag1.pmc|pident) , data = emaD)
Model_PA_posE_groups2 <- lmer(posA ~ posE.pmc + posA_lag1.pmc + depgroup2 + posE.pm + depgroup2*posE.pmc + depgroup2*posE.pm + (posE.pmc + posA_lag1.pmc|pident) , data = emaD)
# p-value correction using false discovery rate
p_values <- data.frame(model = c(rep("Model_NA_negE", 4),
rep("Model_NA_posE", 4),
rep("Model_PA_negE", 4),
rep("Model_PA_posE", 4),
rep("Model_NA_negE_groups", 10),
rep("Model_NA_negE_groups2", 10),
rep("Model_NA_posE_groups", 10),
rep("Model_NA_posE_groups2", 10),
rep("Model_PA_negE_groups", 10),
rep("Model_PA_negE_groups2", 10),
rep("Model_PA_posE_groups", 10),
rep("Model_PA_posE_groups2", 10)
),
p = c(summary(Model_NA_negE)$coefficients[,5],
summary(Model_NA_posE)$coefficients[,5],
summary(Model_PA_negE)$coefficients[,5],
summary(Model_PA_posE)$coefficients[,5],
summary(Model_NA_negE_groups)$coefficients[,5],
summary(Model_NA_negE_groups2)$coefficients[,5],
summary(Model_NA_posE_groups)$coefficients[,5],
summary(Model_NA_posE_groups2)$coefficients[,5],
summary(Model_PA_negE_groups)$coefficients[,5],
summary(Model_PA_negE_groups2)$coefficients[,5],
summary(Model_PA_posE_groups)$coefficients[,5],
summary(Model_PA_posE_groups2)$coefficients[,5]
)
)
p_values$p_adj <- p.adjust(p_values$p, method = "BH")
Main effect models
Displayed p-values are corrected for false discovery rate
NA-reactivity to negative events
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## negA ~ negE.pmc + negA_lag1.pmc + negE.pm + (negE.pmc + negA_lag1.pmc |
## pident)
## Data: emaD
##
## REML criterion at convergence: 19941.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -6.542 -0.439 -0.096 0.277 7.543
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## pident (Intercept) 0.4598 0.678
## negE.pmc 0.1228 0.350 0.30
## negA_lag1.pmc 0.0279 0.167 0.43 0.23
## Residual 0.1658 0.407
## Number of obs: 16845, groups: pident, 346
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.4728 0.0517 371.2078 9.15 < 2e-16 ***
## negE.pmc 0.4034 0.0231 279.1865 17.46 < 2e-16 ***
## negA_lag1.pmc 0.2877 0.0124 325.7226 23.15 < 2e-16 ***
## negE.pm 1.1382 0.2839 345.4822 4.01 0.00014 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) ngE.pmc ngA_1.
## negE.pmc 0.144
## ngA_lg1.pmc 0.194 0.128
## negE.pm -0.706 0.046 0.034
NA-reactivity to positive events
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## negA ~ posE.pmc + negA_lag1.pmc + posE.pm + (posE.pmc + negA_lag1.pmc |
## pident)
## Data: emaD
##
## REML criterion at convergence: 21171.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.894 -0.446 -0.116 0.250 8.882
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## pident (Intercept) 0.4650 0.682
## posE.pmc 0.0237 0.154 -0.69
## negA_lag1.pmc 0.0264 0.162 0.38 -0.32
## Residual 0.1818 0.426
## Number of obs: 16845, groups: pident, 346
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.7785 0.0591 400.1066 13.16 <2e-16 ***
## posE.pmc -0.1494 0.0118 310.3589 -12.68 <2e-16 ***
## negA_lag1.pmc 0.2932 0.0124 328.6077 23.56 <2e-16 ***
## posE.pm -0.4781 0.1385 370.2152 -3.45 0.001 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) psE.pmc ngA_1.
## posE.pmc -0.251
## ngA_lg1.pmc 0.169 -0.137
## posE.pm -0.783 -0.065 -0.004
PA-reactivity to negative events
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## posA ~ negE.pmc + posA_lag1.pmc + negE.pm + (negE.pmc + posA_lag1.pmc |
## pident)
## Data: emaD
##
## REML criterion at convergence: 33492.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -6.229 -0.504 0.073 0.574 4.446
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## pident (Intercept) 0.6440 0.802
## negE.pmc 0.1592 0.399 0.23
## posA_lag1.pmc 0.0184 0.136 -0.26 0.04
## Residual 0.3776 0.614
## Number of obs: 16845, groups: pident, 346
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 4.0076 0.0628 354.1361 63.86 < 2e-16 ***
## negE.pmc -0.5675 0.0286 274.3861 -19.84 < 2e-16 ***
## posA_lag1.pmc 0.3259 0.0105 344.9856 30.98 < 2e-16 ***
## negE.pm -1.7115 0.3529 339.2434 -4.85 3.9e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) ngE.pmc psA_1.
## negE.pmc 0.085
## psA_lg1.pmc -0.113 0.038
## negE.pm -0.722 0.047 -0.014
PA-reactivity to positive events
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## posA ~ posE.pmc + posA_lag1.pmc + posE.pm + (posE.pmc + posA_lag1.pmc |
## pident)
## Data: emaD
##
## REML criterion at convergence: 33933.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -6.091 -0.478 0.094 0.579 3.833
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## pident (Intercept) 0.6659 0.816
## posE.pmc 0.0537 0.232 -0.51
## posA_lag1.pmc 0.0191 0.138 -0.17 -0.08
## Residual 0.3901 0.625
## Number of obs: 16845, groups: pident, 346
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 3.6014 0.0741 371.0162 48.6 <2e-16 ***
## posE.pmc 0.3428 0.0176 313.1215 19.4 <2e-16 ***
## posA_lag1.pmc 0.3126 0.0107 342.8077 29.1 <2e-16 ***
## posE.pm 0.5517 0.1780 355.3781 3.1 0.0033 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) psE.pmc psA_1.
## posE.pmc -0.174
## psA_lg1.pmc -0.067 -0.088
## posE.pm -0.803 -0.050 -0.004
Models estimating group differences
Displayed p-values are corrected for false discovery rate. The control group is the reference group.
NA-reactivity to negative events
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: negA ~ negE.pmc + negA_lag1.pmc + depgroup + negE.pm + depgroup *
## negE.pmc + depgroup * negE.pm + (negE.pmc + negA_lag1.pmc | pident)
## Data: emaD
## Control: lmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 2e+06))
##
## REML criterion at convergence: 19865
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -6.547 -0.434 -0.093 0.278 7.540
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## pident (Intercept) 0.3430 0.586
## negE.pmc 0.1153 0.340 0.21
## negA_lag1.pmc 0.0272 0.165 0.31 0.15
## Residual 0.1659 0.407
## Number of obs: 16845, groups: pident, 346
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.2100 0.0851 352.6675 2.47 0.01955 *
## negE.pmc 0.2782 0.0449 293.8440 6.19 4.5e-09 ***
## negA_lag1.pmc 0.2979 0.0124 325.5817 23.99 < 2e-16 ***
## depgroupremitted 0.2085 0.1060 352.2634 1.97 0.06484 .
## depgroupcurrent 1.0716 0.1354 346.7407 7.91 8.8e-14 ***
## negE.pm 0.6130 0.6378 347.0996 0.96 0.36552
## negE.pmc:depgroupremitted 0.1420 0.0540 284.4114 2.63 0.01303 *
## negE.pmc:depgroupcurrent 0.2486 0.0691 279.6886 3.60 0.00067 ***
## depgroupremitted:negE.pm 0.7021 0.7220 346.0659 0.97 0.36552
## depgroupcurrent:negE.pm -1.0293 0.8251 343.7937 -1.25 0.24064
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) ngE.pmc ngA_1. dpgrpr dpgrpc negE.pm ngE.pmc:dpgrpr
## negE.pmc 0.077
## ngA_lg1.pmc 0.041 0.022
## depgrprmttd -0.800 -0.061 0.032
## depgrpcrrnt -0.626 -0.047 0.045 0.505
## negE.pm -0.717 0.043 0.013 0.576 0.451
## ngE.pmc:dpgrpr -0.063 -0.831 0.016 0.078 0.041 -0.035
## ngE.pmc:dpgrpc -0.049 -0.650 0.025 0.041 0.089 -0.027 0.541
## dpgrprmt:E. 0.633 -0.038 -0.006 -0.719 -0.398 -0.883 0.040
## dpgrpcrr:E. 0.554 -0.033 -0.008 -0.445 -0.704 -0.773 0.027
## ngE.pmc:dpgrpc dpgrpr:E.
## negE.pmc
## ngA_lg1.pmc
## depgrprmttd
## depgrpcrrnt
## negE.pm
## ngE.pmc:dpgrpr
## ngE.pmc:dpgrpc
## dpgrprmt:E. 0.024
## dpgrpcrr:E. 0.028 0.683
NA-reactivity to positive events
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: negA ~ posE.pmc + negA_lag1.pmc + depgroup + posE.pm + depgroup *
## posE.pmc + depgroup * posE.pm + (posE.pmc + negA_lag1.pmc | pident)
## Data: emaD
##
## REML criterion at convergence: 21098.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.884 -0.452 -0.115 0.253 8.878
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## pident (Intercept) 0.3482 0.590
## posE.pmc 0.0202 0.142 -0.62
## negA_lag1.pmc 0.0257 0.160 0.27 -0.24
## Residual 0.1818 0.426
## Number of obs: 16845, groups: pident, 346
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.3224 0.0971 379.0579 3.32 0.00163 **
## posE.pmc -0.0650 0.0219 323.1161 -2.97 0.00494 **
## negA_lag1.pmc 0.3028 0.0124 329.6284 24.37 < 2e-16 ***
## depgroupremitted 0.3821 0.1217 378.2093 3.14 0.00297 **
## depgroupcurrent 1.1785 0.1540 373.8921 7.65 4.2e-13 ***
## posE.pm -0.1786 0.2207 367.9882 -0.81 0.44207
## posE.pmc:depgroupremitted -0.1040 0.0266 313.8397 -3.91 0.00021 ***
## posE.pmc:depgroupcurrent -0.1564 0.0350 323.9748 -4.47 2.1e-05 ***
## depgroupremitted:posE.pm -0.1214 0.2810 365.3701 -0.43 0.67288
## depgroupcurrent:posE.pm -0.7547 0.3980 360.8569 -1.90 0.07416 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) psE.pmc ngA_1. dpgrpr dpgrpc posE.pm psE.pmc:dpgrpr
## posE.pmc -0.208
## ngA_lg1.pmc 0.041 -0.025
## depgrprmttd -0.796 0.164 0.017
## depgrpcrrnt -0.628 0.130 0.028 0.503
## posE.pm -0.786 -0.058 -0.005 0.627 0.495
## psE.pmc:dpgrpr 0.169 -0.823 -0.023 -0.201 -0.108 0.048
## psE.pmc:dpgrpc 0.128 -0.625 -0.023 -0.104 -0.211 0.037 0.516
## dpgrprmt:E. 0.618 0.046 0.007 -0.797 -0.389 -0.786 -0.060
## dpgrpcrr:E. 0.436 0.032 0.004 -0.348 -0.781 -0.555 -0.027
## psE.pmc:dpgrpc dpgrpr:E.
## posE.pmc
## ngA_lg1.pmc
## depgrprmttd
## depgrpcrrnt
## posE.pm
## psE.pmc:dpgrpr
## psE.pmc:dpgrpc
## dpgrprmt:E. -0.029
## dpgrpcrr:E. -0.053 0.436
PA-reactivity to negative events
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: posA ~ negE.pmc + posA_lag1.pmc + depgroup + negE.pm + depgroup *
## negE.pmc + depgroup * negE.pm + (negE.pmc + posA_lag1.pmc | pident)
## Data: emaD
##
## REML criterion at convergence: 33401.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -6.207 -0.504 0.070 0.576 4.454
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## pident (Intercept) 0.4699 0.685
## negE.pmc 0.1592 0.399 0.18
## posA_lag1.pmc 0.0182 0.135 -0.10 0.08
## Residual 0.3776 0.614
## Number of obs: 16845, groups: pident, 346
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 4.5159 0.1014 344.2710 44.54 < 2e-16 ***
## negE.pmc -0.4997 0.0573 297.5819 -8.72 6.2e-16 ***
## posA_lag1.pmc 0.3284 0.0105 344.9457 31.22 < 2e-16 ***
## depgroupremitted -0.5421 0.1268 343.8007 -4.28 4.7e-05 ***
## depgroupcurrent -1.5230 0.1631 343.1374 -9.34 < 2e-16 ***
## negE.pm -1.9858 0.7660 340.3578 -2.59 0.014 *
## negE.pmc:depgroupremitted -0.0745 0.0687 284.4730 -1.08 0.312
## negE.pmc:depgroupcurrent -0.1480 0.0879 280.7951 -1.68 0.111
## depgroupremitted:negE.pm 0.3983 0.8681 339.8941 0.46 0.660
## depgroupcurrent:negE.pm 2.3878 0.9963 341.7719 2.40 0.023 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) ngE.pmc psA_1. dpgrpr dpgrpc negE.pm ngE.pmc:dpgrpr
## negE.pmc 0.058
## psA_lg1.pmc -0.018 0.027
## depgrprmttd -0.800 -0.047 -0.004
## depgrpcrrnt -0.621 -0.037 -0.005 0.497
## negE.pm -0.719 0.046 -0.003 0.575 0.447
## ngE.pmc:dpgrpr -0.049 -0.834 0.001 0.061 0.031 -0.038
## ngE.pmc:dpgrpc -0.038 -0.652 0.001 0.031 0.077 -0.030 0.544
## dpgrprmt:E. 0.635 -0.041 0.001 -0.720 -0.394 -0.882 0.044
## dpgrpcrr:E. 0.553 -0.035 0.001 -0.442 -0.706 -0.769 0.030
## ngE.pmc:dpgrpc dpgrpr:E.
## negE.pmc
## psA_lg1.pmc
## depgrprmttd
## depgrpcrrnt
## negE.pm
## ngE.pmc:dpgrpr
## ngE.pmc:dpgrpc
## dpgrprmt:E. 0.027
## dpgrpcrr:E. 0.031 0.678
PA-reactivity to positive events
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: posA ~ posE.pmc + posA_lag1.pmc + depgroup + posE.pm + depgroup *
## posE.pmc + depgroup * posE.pm + (posE.pmc + posA_lag1.pmc | pident)
## Data: emaD
##
## REML criterion at convergence: 33841.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -6.064 -0.479 0.091 0.576 3.825
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## pident (Intercept) 0.4849 0.696
## posE.pmc 0.0503 0.224 -0.44
## posA_lag1.pmc 0.0191 0.138 -0.04 -0.14
## Residual 0.3902 0.625
## Number of obs: 16845, groups: pident, 346
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 4.2978 0.1185 358.3443 36.26 < 2e-16 ***
## posE.pmc 0.2416 0.0334 329.1922 7.24 7.8e-12 ***
## posA_lag1.pmc 0.3149 0.0107 342.8788 29.33 < 2e-16 ***
## depgroupremitted -0.6790 0.1492 358.0740 -4.55 1.5e-05 ***
## depgroupcurrent -1.5700 0.1895 358.1907 -8.28 6.8e-15 ***
## posE.pm 0.0971 0.2727 352.5355 0.36 0.72203
## posE.pmc:depgroupremitted 0.1252 0.0405 318.6305 3.09 0.00341 **
## posE.pmc:depgroupcurrent 0.1886 0.0532 326.7822 3.55 0.00077 ***
## depgroupremitted:posE.pm 0.2833 0.3485 351.2659 0.81 0.44207
## depgroupcurrent:posE.pm 0.9994 0.4965 351.8726 2.01 0.05903 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) psE.pmc psA_1. dpgrpr dpgrpc posE.pm psE.pmc:dpgrpr
## posE.pmc -0.151
## psA_lg1.pmc -0.007 -0.055
## depgrprmttd -0.795 0.120 -0.001
## depgrpcrrnt -0.625 0.095 -0.001 0.497
## posE.pm -0.797 -0.042 -0.001 0.633 0.498
## psE.pmc:dpgrpr 0.125 -0.822 -0.006 -0.148 -0.078 0.035
## psE.pmc:dpgrpc 0.095 -0.625 -0.016 -0.076 -0.158 0.027 0.516
## dpgrprmt:E. 0.623 0.033 -0.001 -0.808 -0.390 -0.783 -0.043
## dpgrpcrr:E. 0.438 0.023 -0.001 -0.348 -0.791 -0.549 -0.019
## psE.pmc:dpgrpc dpgrpr:E.
## posE.pmc
## psA_lg1.pmc
## depgrprmttd
## depgrpcrrnt
## posE.pm
## psE.pmc:dpgrpr
## psE.pmc:dpgrpc
## dpgrprmt:E. -0.021
## dpgrpcrr:E. -0.037 0.430
Models estimating group differences with current group as reference group
Displayed p-values are corrected for false discovery rate.
NA-reactivity to negative events
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: negA ~ negE.pmc + negA_lag1.pmc + depgroup2 + negE.pm + depgroup2 *
## negE.pmc + depgroup2 * negE.pm + (negE.pmc + negA_lag1.pmc | pident)
## Data: emaD
## Control: lmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 2e+06))
##
## REML criterion at convergence: 19865
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -6.547 -0.434 -0.093 0.278 7.540
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## pident (Intercept) 0.3430 0.586
## negE.pmc 0.1153 0.340 0.21
## negA_lag1.pmc 0.0272 0.165 0.31 0.15
## Residual 0.1659 0.407
## Number of obs: 16845, groups: pident, 346
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.2816 0.1056 340.8637 12.13 < 2e-16 ***
## negE.pmc 0.5268 0.0525 269.3857 10.03 < 2e-16 ***
## negA_lag1.pmc 0.2979 0.0124 325.5819 23.99 < 2e-16 ***
## depgroup2remitted -0.8631 0.1228 339.2484 -7.03 2.7e-11 ***
## depgroup2control -1.0716 0.1354 346.7403 -7.91 8.8e-14 ***
## negE.pm -0.4163 0.5235 332.3737 -0.80 0.44565
## negE.pmc:depgroup2remitted -0.1067 0.0604 267.6464 -1.76 0.09822 .
## negE.pmc:depgroup2control -0.2486 0.0691 279.6884 -3.60 0.00067 ***
## depgroup2remitted:negE.pm 1.7313 0.6234 336.1270 2.78 0.00868 **
## depgroup2control:negE.pm 1.0293 0.8251 343.7934 1.25 0.24064
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) ngE.pmc ngA_1. dpgrp2r dpgrp2c negE.pm ngE.pmc:dpgrp2r
## negE.pmc 0.100
## ngA_lg1.pmc 0.090 0.051
## dpgrp2rmttd -0.855 -0.083 -0.022
## dpgrp2cntrl -0.778 -0.077 -0.045 0.667
## negE.pm -0.719 0.013 0.002 0.618 0.561
## ngE.pmc:dpgrp2r -0.084 -0.867 -0.014 0.092 0.065 -0.012
## ngE.pmc:dpgrp2c -0.075 -0.759 -0.025 0.063 0.089 -0.010 0.659
## dpgrp2rm:E. 0.604 -0.011 0.005 -0.730 -0.471 -0.840 0.019
## dpgrp2cn:E. 0.457 -0.008 0.008 -0.393 -0.704 -0.634 0.007
## ngE.pmc:dpgrp2c dpgrp2r:E.
## negE.pmc
## ngA_lg1.pmc
## dpgrp2rmttd
## dpgrp2cntrl
## negE.pm
## ngE.pmc:dpgrp2r
## ngE.pmc:dpgrp2c
## dpgrp2rm:E. 0.008
## dpgrp2cn:E. 0.028 0.533
NA-reactivity to positive events
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: negA ~ posE.pmc + negA_lag1.pmc + depgroup2 + posE.pm + depgroup2 *
## posE.pmc + depgroup2 * posE.pm + (posE.pmc + negA_lag1.pmc | pident)
## Data: emaD
##
## REML criterion at convergence: 21098.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.884 -0.452 -0.115 0.253 8.878
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## pident (Intercept) 0.3482 0.590
## posE.pmc 0.0202 0.142 -0.62
## negA_lag1.pmc 0.0257 0.160 0.27 -0.24
## Residual 0.1818 0.426
## Number of obs: 16845, groups: pident, 346
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.5009 0.1198 369.1824 12.53 < 2e-16 ***
## posE.pmc -0.2214 0.0273 324.4995 -8.12 2.8e-14 ***
## negA_lag1.pmc 0.3028 0.0124 329.6284 24.37 < 2e-16 ***
## depgroup2remitted -0.7964 0.1403 368.1889 -5.68 6.1e-08 ***
## depgroup2control -1.1785 0.1540 373.8921 -7.65 4.2e-13 ***
## posE.pm -0.9333 0.3311 353.4582 -2.82 0.0078 **
## posE.pmc:depgroup2remitted 0.0524 0.0311 315.8165 1.68 0.1108
## posE.pmc:depgroup2control 0.1564 0.0350 323.9748 4.47 2.1e-05 ***
## depgroup2remitted:posE.pm 0.6333 0.3740 354.8576 1.69 0.1108
## depgroup2control:posE.pm 0.7547 0.3980 360.8568 1.90 0.0742 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) psE.pmc ngA_1. dpgrp2r dpgrp2c posE.pm psE.pmc:dpgrp2r
## posE.pmc -0.215
## ngA_lg1.pmc 0.069 -0.050
## dpgrp2rmttd -0.851 0.181 -0.016
## dpgrp2cntrl -0.776 0.166 -0.028 0.662
## posE.pm -0.783 -0.050 0.001 0.668 0.609
## psE.pmc:dpgrp2r 0.186 -0.874 0.007 -0.206 -0.144 0.044
## psE.pmc:dpgrp2c 0.167 -0.780 0.023 -0.141 -0.211 0.039 0.682
## dpgrp2rm:E. 0.693 0.044 0.001 -0.791 -0.539 -0.885 -0.053
## dpgrp2cn:E. 0.651 0.042 -0.004 -0.556 -0.781 -0.832 -0.037
## psE.pmc:dpgrp2c dpgrp2r:E.
## posE.pmc
## ngA_lg1.pmc
## dpgrp2rmttd
## dpgrp2cntrl
## posE.pm
## psE.pmc:dpgrp2r
## psE.pmc:dpgrp2c
## dpgrp2rm:E. -0.035
## dpgrp2cn:E. -0.053 0.737
PA-reactivity to negative events
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: posA ~ negE.pmc + posA_lag1.pmc + depgroup2 + negE.pm + depgroup2 *
## negE.pmc + depgroup2 * negE.pm + (negE.pmc + posA_lag1.pmc | pident)
## Data: emaD
##
## REML criterion at convergence: 33401.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -6.207 -0.504 0.070 0.576 4.454
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## pident (Intercept) 0.4699 0.685
## negE.pmc 0.1592 0.399 0.18
## posA_lag1.pmc 0.0182 0.135 -0.10 0.08
## Residual 0.3776 0.614
## Number of obs: 16845, groups: pident, 346
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.9929 0.1278 342.4372 23.42 < 2e-16 ***
## negE.pmc -0.6476 0.0667 270.2223 -9.71 < 2e-16 ***
## posA_lag1.pmc 0.3284 0.0105 344.9458 31.22 < 2e-16 ***
## depgroup2remitted 0.9809 0.1487 342.5059 6.60 3.6e-10 ***
## depgroup2control 1.5230 0.1631 343.1374 9.34 < 2e-16 ***
## negE.pm 0.4020 0.6371 342.2618 0.63 0.546
## negE.pmc:depgroup2remitted 0.0735 0.0767 266.9850 0.96 0.366
## negE.pmc:depgroup2control 0.1480 0.0879 280.7952 1.68 0.111
## depgroup2remitted:negE.pm -1.9895 0.7568 341.6444 -2.63 0.013 *
## depgroup2control:negE.pm -2.3878 0.9963 341.7719 -2.40 0.023 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) ngE.pmc psA_1. dpgrp2r dpgrp2c negE.pm ngE.pmc:dpgrp2r
## negE.pmc 0.088
## psA_lg1.pmc -0.020 0.024
## dpgrp2rmttd -0.859 -0.076 0.002
## dpgrp2cntrl -0.783 -0.070 0.005 0.673
## negE.pm -0.722 0.015 -0.002 0.621 0.566
## ngE.pmc:dpgrp2r -0.077 -0.869 0.000 0.083 0.061 -0.013
## ngE.pmc:dpgrp2c -0.067 -0.758 -0.001 0.058 0.077 -0.012 0.659
## dpgrp2rm:E. 0.608 -0.013 0.000 -0.730 -0.476 -0.842 0.022
## dpgrp2cn:E. 0.462 -0.010 -0.001 -0.397 -0.706 -0.639 0.009
## ngE.pmc:dpgrp2c dpgrp2r:E.
## negE.pmc
## psA_lg1.pmc
## dpgrp2rmttd
## dpgrp2cntrl
## negE.pm
## ngE.pmc:dpgrp2r
## ngE.pmc:dpgrp2c
## dpgrp2rm:E. 0.010
## dpgrp2cn:E. 0.031 0.538
PA-reactivity to positive events
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: posA ~ posE.pmc + posA_lag1.pmc + depgroup2 + posE.pm + depgroup2 *
## posE.pmc + depgroup2 * posE.pm + (posE.pmc + posA_lag1.pmc | pident)
## Data: emaD
##
## REML criterion at convergence: 33841.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -6.064 -0.479 0.091 0.576 3.825
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## pident (Intercept) 0.4849 0.696
## posE.pmc 0.0503 0.224 -0.44
## posA_lag1.pmc 0.0191 0.138 -0.04 -0.14
## Residual 0.3902 0.625
## Number of obs: 16845, groups: pident, 346
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.7279 0.1479 358.0339 18.44 < 2e-16 ***
## posE.pmc 0.4302 0.0415 327.9437 10.36 < 2e-16 ***
## posA_lag1.pmc 0.3149 0.0107 342.8789 29.33 < 2e-16 ***
## depgroup2remitted 0.8910 0.1734 357.8377 5.14 9.8e-07 ***
## depgroup2control 1.5700 0.1895 358.1907 8.28 6.8e-15 ***
## posE.pm 1.0965 0.4149 351.3639 2.64 0.01268 *
## posE.pmc:depgroup2remitted -0.0634 0.0474 319.2013 -1.34 0.21074
## posE.pmc:depgroup2control -0.1886 0.0532 326.7822 -3.55 0.00077 ***
## depgroup2remitted:posE.pm -0.7161 0.4682 350.8748 -1.53 0.14876
## depgroup2control:posE.pm -0.9994 0.4965 351.8726 -2.01 0.05903 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) psE.pmc psA_1. dpgrp2r dpgrp2c posE.pm psE.pmc:dpgrp2r
## posE.pmc -0.161
## psA_lg1.pmc -0.007 -0.064
## dpgrp2rmttd -0.853 0.138 0.001
## dpgrp2cntrl -0.780 0.126 0.001 0.665
## posE.pm -0.794 -0.034 -0.002 0.677 0.620
## psE.pmc:dpgrp2r 0.142 -0.873 0.013 -0.157 -0.111 0.030
## psE.pmc:dpgrp2c 0.126 -0.779 0.016 -0.108 -0.158 0.027 0.682
## dpgrp2rm:E. 0.704 0.030 0.000 -0.800 -0.549 -0.886 -0.037
## dpgrp2cn:E. 0.664 0.028 0.001 -0.566 -0.791 -0.836 -0.025
## psE.pmc:dpgrp2c dpgrp2r:E.
## posE.pmc
## psA_lg1.pmc
## dpgrp2rmttd
## dpgrp2cntrl
## posE.pm
## psE.pmc:dpgrp2r
## psE.pmc:dpgrp2c
## dpgrp2rm:E. -0.024
## dpgrp2cn:E. -0.037 0.740
Analysis step 4: Residual plots for replication models (DHARMa package)
NA-reactivity to negative events
DHARMa_simres <- simulateResiduals(fittedModel = Model_NA_negE_groups)
par(mfrow=c(1,2))
plotQQunif(DHARMa_simres, testUniformity = F, testOutliers = F, testDispersion = F)
plotResiduals(DHARMa_simres, rank = T, smoothScatter = F)

NA-reactivity to positive events
DHARMa_simres <- simulateResiduals(fittedModel = Model_NA_posE_groups)
par(mfrow=c(1,2))
plotQQunif(DHARMa_simres, testUniformity = F, testOutliers = F, testDispersion = F)
plotResiduals(DHARMa_simres, rank = T, smoothScatter = F)

PA-reactivity to negative events
DHARMa_simres <- simulateResiduals(fittedModel = Model_PA_negE_groups)
par(mfrow=c(1,2))
plotQQunif(DHARMa_simres, testUniformity = F, testOutliers = F, testDispersion = F)
plotResiduals(DHARMa_simres, rank = T, smoothScatter = F)

PA-reactivity to positive events
DHARMa_simres <- simulateResiduals(fittedModel = Model_PA_posE_groups)
par(mfrow=c(1,2))
plotQQunif(DHARMa_simres, testUniformity = F, testOutliers = F, testDispersion = F)
plotResiduals(DHARMa_simres, rank = T, smoothScatter = F)

Analysis step 5: Two-part models of negative affect
Random intercept only
Negative events
NA_negE_twopart_1 <- mixed_model(fixed=negA ~ negE.pmc + negA_lag1.pmc + depgroup + negE.pm + depgroup*negE.pmc + depgroup*negE.pm, random=~1 |pident,
data = emaD,
zi_fixed = ~ negE.pmc + negA_lag1.pmc + depgroup + negE.pm + depgroup*negE.pmc + depgroup*negE.pm,
zi_random= NULL,
family = hurdle.lognormal(), n_phis=1, control=(iter_EM = 0))
model overview
## $logLik
## 'log Lik.' -20036.58 (df=22)
##
## $AIC
## [1] 40117.15
##
## $BIC
## [1] 40201.77
##
## $call
## mixed_model(fixed = negA ~ negE.pmc + negA_lag1.pmc + depgroup +
## negE.pm + depgroup * negE.pmc + depgroup * negE.pm, random = ~1 |
## pident, data = emaD, family = hurdle.lognormal(), zi_fixed = ~negE.pmc +
## negA_lag1.pmc + depgroup + negE.pm + depgroup * negE.pmc +
## depgroup * negE.pm, zi_random = NULL, n_phis = 1, control = (iter_EM = 0))
##
## $N
## [1] 16845
##
## $phis_table
## Estimate Std.Err
## phi_1 -0.508282 0.006967357
##
## $family
##
## Family: two-part log-normal
## Link function: identity
coefficients for the continuous part
## Estimate Std.Err z-value p-value
## (Intercept) -1.2198 0.0978 -12.4754 0.0000
## negE.pmc 0.3986 0.0409 9.7487 0.0000
## negA_lag1.pmc 0.3400 0.0102 33.1858 0.0000
## depgroupremitted 0.3810 0.1201 3.1739 0.0015
## depgroupcurrent 1.2949 0.1509 8.5837 0.0000
## negE.pm 0.5796 0.7142 0.8116 0.4170
## negE.pmc:depgroupremitted -0.0007 0.0465 -0.0146 0.9883
## negE.pmc:depgroupcurrent 0.0260 0.0556 0.4678 0.6400
## depgroupremitted:negE.pm 0.3172 0.8057 0.3936 0.6939
## depgroupcurrent:negE.pm -1.3544 0.9168 -1.4772 0.1396
coefficients for the zero-inflated part
## Estimate Std.Err z-value p-value
## (Intercept) 0.6570 0.0459 14.3183 0.0000
## negE.pmc -1.0438 0.1145 -9.1150 0.0000
## negA_lag1.pmc -0.6076 0.0415 -14.6492 0.0000
## depgroupremitted -0.9114 0.0574 -15.8772 0.0000
## depgroupcurrent -2.9336 0.0930 -31.5505 0.0000
## negE.pm -3.8045 0.3683 -10.3305 0.0000
## negE.pmc:depgroupremitted 0.1222 0.1432 0.8535 0.3934
## negE.pmc:depgroupcurrent 0.1912 0.2598 0.7358 0.4619
## depgroupremitted:negE.pm 0.5488 0.4267 1.2863 0.1983
## depgroupcurrent:negE.pm 5.0871 0.5087 9.9996 0.0000
exponential of coefficients for the zero-inflated part
## (Intercept) negE.pmc negA_lag1.pmc
## 1.9290 0.3521 0.5447
## depgroupremitted depgroupcurrent negE.pm
## 0.4019 0.0532 0.0223
## negE.pmc:depgroupremitted negE.pmc:depgroupcurrent depgroupremitted:negE.pm
## 1.1300 1.2107 1.7312
## depgroupcurrent:negE.pm
## 161.9253
random effects (intercept and slope (co)variance)
## $D
## (Intercept)
## (Intercept) 0.3725
## attr(,"L")
## [1] 0.6103
Positive events
NA_posE_twopart_1 <- mixed_model(fixed=negA ~ posE.pmc + negA_lag1.pmc + depgroup + posE.pm + depgroup*posE.pmc + depgroup*posE.pm, random=~1 |pident,
data = emaD,
zi_fixed = ~ posE.pmc + negA_lag1.pmc + depgroup + posE.pm + depgroup*posE.pmc + depgroup*posE.pm,
zi_random= NULL,
family = hurdle.lognormal(), n_phis=1, control=(iter_EM = 0))
model overview
## $logLik
## 'log Lik.' -20431.54 (df=22)
##
## $AIC
## [1] 40907.07
##
## $BIC
## [1] 40991.69
##
## $call
## mixed_model(fixed = negA ~ posE.pmc + negA_lag1.pmc + depgroup +
## posE.pm + depgroup * posE.pmc + depgroup * posE.pm, random = ~1 |
## pident, data = emaD, family = hurdle.lognormal(), zi_fixed = ~posE.pmc +
## negA_lag1.pmc + depgroup + posE.pm + depgroup * posE.pmc +
## depgroup * posE.pm, zi_random = NULL, n_phis = 1, control = (iter_EM = 0))
##
## $N
## [1] 16845
##
## $phis_table
## Estimate Std.Err
## phi_1 -0.4902802 0.006966042
##
## $family
##
## Family: two-part log-normal
## Link function: identity
coefficients for the continuous part
## Estimate Std.Err z-value p-value
## (Intercept) -0.9548 0.1128 -8.4686 0.0000
## posE.pmc -0.1090 0.0360 -3.0281 0.0025
## negA_lag1.pmc 0.3372 0.0105 32.2579 0.0000
## depgroupremitted 0.3203 0.1395 2.2963 0.0217
## depgroupcurrent 1.1270 0.1733 6.5021 0.0000
## posE.pm -0.5049 0.2649 -1.9061 0.0566
## posE.pmc:depgroupremitted -0.1012 0.0407 -2.4877 0.0129
## posE.pmc:depgroupcurrent -0.0803 0.0471 -1.7075 0.0877
## depgroupremitted:posE.pm 0.3230 0.3315 0.9743 0.3299
## depgroupcurrent:posE.pm -0.2283 0.4589 -0.4976 0.6188
coefficients for the zero-inflated part
## Estimate Std.Err z-value p-value
## (Intercept) 0.1458 0.0509 2.8623 0.0042
## posE.pmc 0.5496 0.0758 7.2488 0.0000
## negA_lag1.pmc -0.5775 0.0405 -14.2651 0.0000
## depgroupremitted -0.8204 0.0652 -12.5917 0.0000
## depgroupcurrent -2.6642 0.1134 -23.5007 0.0000
## posE.pm 0.4348 0.1197 3.6314 0.0003
## posE.pmc:depgroupremitted -0.1522 0.0920 -1.6537 0.0982
## posE.pmc:depgroupcurrent -0.0991 0.1535 -0.6455 0.5186
## depgroupremitted:posE.pm -0.4137 0.1552 -2.6658 0.0077
## depgroupcurrent:posE.pm 1.0075 0.2753 3.6595 0.0003
exponential of coefficients for the zero-inflated part
## (Intercept) posE.pmc negA_lag1.pmc
## 1.1570 1.7325 0.5613
## depgroupremitted depgroupcurrent posE.pm
## 0.4402 0.0697 1.5447
## posE.pmc:depgroupremitted posE.pmc:depgroupcurrent depgroupremitted:posE.pm
## 0.8588 0.9057 0.6612
## depgroupcurrent:posE.pm
## 2.7387
random effects (intercept SD and variance)
## $D
## (Intercept)
## (Intercept) 0.3605
## attr(,"L")
## [1] 0.6004
Random intercept and event slope
Negative events
NA_negE_twopart_2 <- mixed_model(fixed=negA ~ negE.pmc + negA_lag1.pmc + depgroup + negE.pm + depgroup*negE.pmc + depgroup*negE.pm, random=~1 + negE.pmc|pident,
data = emaD,
zi_fixed = ~ negE.pmc + negA_lag1.pmc + depgroup + negE.pm + depgroup*negE.pmc + depgroup*negE.pm,
zi_random= NULL,
family = hurdle.lognormal(), n_phis=1, control=(iter_EM = 0))
model overview
## $logLik
## 'log Lik.' -20005.3 (df=24)
##
## $AIC
## [1] 40058.59
##
## $BIC
## [1] 40150.91
##
## $call
## mixed_model(fixed = negA ~ negE.pmc + negA_lag1.pmc + depgroup +
## negE.pm + depgroup * negE.pmc + depgroup * negE.pm, random = ~1 +
## negE.pmc | pident, data = emaD, family = hurdle.lognormal(),
## zi_fixed = ~negE.pmc + negA_lag1.pmc + depgroup + negE.pm +
## depgroup * negE.pmc + depgroup * negE.pm, zi_random = NULL,
## n_phis = 1, control = (iter_EM = 0))
##
## $N
## [1] 16845
##
## $phis_table
## Estimate Std.Err
## phi_1 -0.5187107 0.007068262
##
## $family
##
## Family: two-part log-normal
## Link function: identity
coefficients for the continuous part
## Estimate Std.Err z-value p-value
## (Intercept) -1.2148 0.0972 -12.4947 0.0000
## negE.pmc 0.4005 0.0531 7.5470 0.0000
## negA_lag1.pmc 0.3409 0.0102 33.3941 0.0000
## depgroupremitted 0.3959 0.1195 3.3123 0.0009
## depgroupcurrent 1.2978 0.1503 8.6319 0.0000
## negE.pm 0.5582 0.7057 0.7910 0.4289
## negE.pmc:depgroupremitted 0.0316 0.0616 0.5126 0.6083
## negE.pmc:depgroupcurrent 0.0314 0.0745 0.4213 0.6735
## depgroupremitted:negE.pm 0.1651 0.7970 0.2072 0.8359
## depgroupcurrent:negE.pm -1.3842 0.9080 -1.5244 0.1274
coefficients for the zero-inflated part
## Estimate Std.Err z-value p-value
## (Intercept) 0.6570 0.0459 14.3183 0.0000
## negE.pmc -1.0438 0.1145 -9.1150 0.0000
## negA_lag1.pmc -0.6076 0.0415 -14.6492 0.0000
## depgroupremitted -0.9114 0.0574 -15.8772 0.0000
## depgroupcurrent -2.9336 0.0930 -31.5505 0.0000
## negE.pm -3.8045 0.3683 -10.3305 0.0000
## negE.pmc:depgroupremitted 0.1222 0.1432 0.8535 0.3934
## negE.pmc:depgroupcurrent 0.1912 0.2598 0.7358 0.4619
## depgroupremitted:negE.pm 0.5488 0.4267 1.2863 0.1983
## depgroupcurrent:negE.pm 5.0871 0.5087 9.9996 0.0000
exponential of coefficients for the zero-inflated part
## (Intercept) negE.pmc negA_lag1.pmc
## 1.9290 0.3521 0.5447
## depgroupremitted depgroupcurrent negE.pm
## 0.4019 0.0532 0.0223
## negE.pmc:depgroupremitted negE.pmc:depgroupcurrent depgroupremitted:negE.pm
## 1.1300 1.2107 1.7312
## depgroupcurrent:negE.pm
## 161.9253
random effects (intercept and slope (co)variance)
## $D
## (Intercept) negE.pmc
## (Intercept) 0.37429 -0.04074
## negE.pmc -0.04074 0.06733
## attr(,"L")
## [1] 0.61180 -0.06659 0.25079
likelihood ratio test
anova(NA_negE_twopart_1, NA_negE_twopart_2)
##
## AIC BIC log.Lik LRT df p.value
## NA_negE_twopart_1 40117.15 40201.77 -20036.58
## NA_negE_twopart_2 40058.59 40150.91 -20005.30 62.56 2 <0.0001
Positive events
NA_posE_twopart_2 <- mixed_model(fixed=negA ~ posE.pmc + negA_lag1.pmc + depgroup + posE.pm + depgroup*posE.pmc + depgroup*posE.pm, random=~1 + posE.pmc |pident,
data = emaD,
zi_fixed = ~ posE.pmc + negA_lag1.pmc + depgroup + posE.pm + depgroup*posE.pmc + depgroup*posE.pm,
zi_random= NULL,
family = hurdle.lognormal(), n_phis=1, control=(iter_EM = 0))
model overview
## $logLik
## 'log Lik.' -20415.88 (df=24)
##
## $AIC
## [1] 40879.76
##
## $BIC
## [1] 40972.07
##
## $call
## mixed_model(fixed = negA ~ posE.pmc + negA_lag1.pmc + depgroup +
## posE.pm + depgroup * posE.pmc + depgroup * posE.pm, random = ~1 +
## posE.pmc | pident, data = emaD, family = hurdle.lognormal(),
## zi_fixed = ~posE.pmc + negA_lag1.pmc + depgroup + posE.pm +
## depgroup * posE.pmc + depgroup * posE.pm, zi_random = NULL,
## n_phis = 1, control = (iter_EM = 0))
##
## $N
## [1] 16845
##
## $phis_table
## Estimate Std.Err
## phi_1 -0.4976181 0.007062241
##
## $family
##
## Family: two-part log-normal
## Link function: identity
coefficients for the continuous part
## Estimate Std.Err z-value p-value
## (Intercept) -0.9692 0.1127 -8.5981 0.0000
## posE.pmc -0.0972 0.0436 -2.2308 0.0257
## negA_lag1.pmc 0.3363 0.0104 32.2404 0.0000
## depgroupremitted 0.3584 0.1406 2.5495 0.0108
## depgroupcurrent 1.1379 0.1730 6.5769 0.0000
## posE.pm -0.4675 0.2648 -1.7655 0.0775
## posE.pmc:depgroupremitted -0.0932 0.0498 -1.8692 0.0616
## posE.pmc:depgroupcurrent -0.0878 0.0587 -1.4961 0.1346
## depgroupremitted:posE.pm 0.2151 0.3355 0.6411 0.5215
## depgroupcurrent:posE.pm -0.2562 0.4576 -0.5599 0.5755
coefficients for the zero-inflated part
## Estimate Std.Err z-value p-value
## (Intercept) 0.1458 0.0509 2.8623 0.0042
## posE.pmc 0.5496 0.0758 7.2488 0.0000
## negA_lag1.pmc -0.5775 0.0405 -14.2651 0.0000
## depgroupremitted -0.8204 0.0652 -12.5917 0.0000
## depgroupcurrent -2.6642 0.1134 -23.5007 0.0000
## posE.pm 0.4348 0.1197 3.6314 0.0003
## posE.pmc:depgroupremitted -0.1522 0.0920 -1.6537 0.0982
## posE.pmc:depgroupcurrent -0.0991 0.1535 -0.6455 0.5186
## depgroupremitted:posE.pm -0.4137 0.1552 -2.6658 0.0077
## depgroupcurrent:posE.pm 1.0075 0.2753 3.6595 0.0003
exponential of coefficients for the zero-inflated part
## (Intercept) posE.pmc negA_lag1.pmc
## 1.1570 1.7325 0.5613
## depgroupremitted depgroupcurrent posE.pm
## 0.4402 0.0697 1.5447
## posE.pmc:depgroupremitted posE.pmc:depgroupcurrent depgroupremitted:posE.pm
## 0.8588 0.9057 0.6612
## depgroupcurrent:posE.pm
## 2.7387
random effects (intercept SD and variance)
## $D
## (Intercept) posE.pmc
## (Intercept) 0.36042 -0.01836
## posE.pmc -0.01836 0.03385
## attr(,"L")
## [1] 0.60035 -0.03058 0.18142
likelihood ratio test
anova(NA_posE_twopart_1, NA_posE_twopart_2)
##
## AIC BIC log.Lik LRT df p.value
## NA_posE_twopart_1 40907.07 40991.69 -20431.54
## NA_posE_twopart_2 40879.76 40972.07 -20415.88 31.31 2 <0.0001
Random intercept, event slope, and negA_lag1 slope
Negative events
NA_negE_twopart_3 <- mixed_model(fixed=negA ~ negE.pmc + negA_lag1.pmc + depgroup + negE.pm + depgroup*negE.pmc + depgroup*negE.pm, random=~1 + negE.pmc + negA_lag1.pmc|pident,
data = emaD,
zi_fixed = ~ negE.pmc + negA_lag1.pmc + depgroup + negE.pm + depgroup*negE.pmc + depgroup*negE.pm,
zi_random= NULL,
family = hurdle.lognormal(), n_phis=1, control=(iter_EM = 0))
model overview
## $logLik
## 'log Lik.' -19948.63 (df=27)
##
## $AIC
## [1] 39951.26
##
## $BIC
## [1] 40055.11
##
## $call
## mixed_model(fixed = negA ~ negE.pmc + negA_lag1.pmc + depgroup +
## negE.pm + depgroup * negE.pmc + depgroup * negE.pm, random = ~1 +
## negE.pmc + negA_lag1.pmc | pident, data = emaD, family = hurdle.lognormal(),
## zi_fixed = ~negE.pmc + negA_lag1.pmc + depgroup + negE.pm +
## depgroup * negE.pmc + depgroup * negE.pm, zi_random = NULL,
## n_phis = 1, control = (iter_EM = 0))
##
## $N
## [1] 16845
##
## $phis_table
## Estimate Std.Err
## phi_1 -0.5303796 0.007152237
##
## $family
##
## Family: two-part log-normal
## Link function: identity
coefficients for the continuous part
## Estimate Std.Err z-value p-value
## (Intercept) -1.2359 0.0956 -12.9300 0.0000
## negE.pmc 0.4040 0.0520 7.7734 0.0000
## negA_lag1.pmc 0.3711 0.0171 21.7025 0.0000
## depgroupremitted 0.4499 0.1173 3.8361 0.0001
## depgroupcurrent 1.3375 0.1445 9.2585 0.0000
## negE.pm 0.8365 0.6892 1.2138 0.2248
## negE.pmc:depgroupremitted 0.0341 0.0603 0.5645 0.5724
## negE.pmc:depgroupcurrent 0.0390 0.0731 0.5334 0.5938
## depgroupremitted:negE.pm -0.3400 0.7813 -0.4352 0.6634
## depgroupcurrent:negE.pm -1.9486 0.8757 -2.2251 0.0261
coefficients for the zero-inflated part
## Estimate Std.Err z-value p-value
## (Intercept) 0.6570 0.0459 14.3183 0.0000
## negE.pmc -1.0438 0.1145 -9.1150 0.0000
## negA_lag1.pmc -0.6076 0.0415 -14.6492 0.0000
## depgroupremitted -0.9114 0.0574 -15.8772 0.0000
## depgroupcurrent -2.9336 0.0930 -31.5505 0.0000
## negE.pm -3.8045 0.3683 -10.3305 0.0000
## negE.pmc:depgroupremitted 0.1222 0.1432 0.8535 0.3934
## negE.pmc:depgroupcurrent 0.1912 0.2598 0.7358 0.4619
## depgroupremitted:negE.pm 0.5488 0.4267 1.2863 0.1983
## depgroupcurrent:negE.pm 5.0871 0.5087 9.9996 0.0000
exponential of coefficients for the zero-inflated part
## (Intercept) negE.pmc negA_lag1.pmc
## 1.9290 0.3521 0.5447
## depgroupremitted depgroupcurrent negE.pm
## 0.4019 0.0532 0.0223
## negE.pmc:depgroupremitted negE.pmc:depgroupcurrent depgroupremitted:negE.pm
## 1.1300 1.2107 1.7312
## depgroupcurrent:negE.pm
## 161.9253
random effects (intercept and slope (co)variance)
## $D
## (Intercept) negE.pmc negA_lag1.pmc
## (Intercept) 0.38003 -0.040974 -0.047577
## negE.pmc -0.04097 0.061364 0.003973
## negA_lag1.pmc -0.04758 0.003973 0.026341
## attr(,"L")
## [1] 0.616467 -0.066466 -0.077177 0.238635 -0.004845 0.142693
likelihood ratio test
anova(NA_negE_twopart_2, NA_negE_twopart_3)
##
## AIC BIC log.Lik LRT df p.value
## NA_negE_twopart_2 40058.59 40150.91 -20005.30
## NA_negE_twopart_3 39951.26 40055.11 -19948.63 113.33 3 <0.0001
Positive events
NA_posE_twopart_3 <- mixed_model(fixed=negA ~ posE.pmc + negA_lag1.pmc + depgroup + posE.pm + depgroup*posE.pmc + depgroup*posE.pm, random=~1 + posE.pmc + negA_lag1.pmc |pident,
data = emaD,
zi_fixed = ~ posE.pmc + negA_lag1.pmc + depgroup + posE.pm + depgroup*posE.pmc + depgroup*posE.pm,
zi_random= NULL,
family = hurdle.lognormal(), n_phis=1, control=(iter_EM = 0))
model overview
## $logLik
## 'log Lik.' -20364.93 (df=27)
##
## $AIC
## [1] 40783.86
##
## $BIC
## [1] 40887.72
##
## $call
## mixed_model(fixed = negA ~ posE.pmc + negA_lag1.pmc + depgroup +
## posE.pm + depgroup * posE.pmc + depgroup * posE.pm, random = ~1 +
## posE.pmc + negA_lag1.pmc | pident, data = emaD, family = hurdle.lognormal(),
## zi_fixed = ~posE.pmc + negA_lag1.pmc + depgroup + posE.pm +
## depgroup * posE.pmc + depgroup * posE.pm, zi_random = NULL,
## n_phis = 1, control = (iter_EM = 0))
##
## $N
## [1] 16845
##
## $phis_table
## Estimate Std.Err
## phi_1 -0.5082036 0.007144654
##
## $family
##
## Family: two-part log-normal
## Link function: identity
coefficients for the continuous part
## Estimate Std.Err z-value p-value
## (Intercept) -0.9842 0.1109 -8.8723 0.0000
## posE.pmc -0.0891 0.0429 -2.0774 0.0378
## negA_lag1.pmc 0.3573 0.0167 21.3798 0.0000
## depgroupremitted 0.3990 0.1375 2.9029 0.0037
## depgroupcurrent 1.1188 0.1672 6.6903 0.0000
## posE.pm -0.4007 0.2616 -1.5316 0.1256
## posE.pmc:depgroupremitted -0.1022 0.0490 -2.0835 0.0372
## posE.pmc:depgroupcurrent -0.0938 0.0578 -1.6223 0.1047
## depgroupremitted:posE.pm 0.0754 0.3291 0.2290 0.8189
## depgroupcurrent:posE.pm -0.2527 0.4393 -0.5752 0.5651
coefficients for the zero-inflated part
## Estimate Std.Err z-value p-value
## (Intercept) 0.1458 0.0509 2.8623 0.0042
## posE.pmc 0.5496 0.0758 7.2488 0.0000
## negA_lag1.pmc -0.5775 0.0405 -14.2651 0.0000
## depgroupremitted -0.8204 0.0652 -12.5917 0.0000
## depgroupcurrent -2.6642 0.1134 -23.5007 0.0000
## posE.pm 0.4348 0.1197 3.6314 0.0003
## posE.pmc:depgroupremitted -0.1522 0.0920 -1.6537 0.0982
## posE.pmc:depgroupcurrent -0.0991 0.1535 -0.6455 0.5186
## depgroupremitted:posE.pm -0.4137 0.1552 -2.6658 0.0077
## depgroupcurrent:posE.pm 1.0075 0.2753 3.6595 0.0003
exponential of coefficients for the zero-inflated part
## (Intercept) posE.pmc negA_lag1.pmc
## 1.1570 1.7325 0.5613
## depgroupremitted depgroupcurrent posE.pm
## 0.4402 0.0697 1.5447
## posE.pmc:depgroupremitted posE.pmc:depgroupcurrent depgroupremitted:posE.pm
## 0.8588 0.9057 0.6612
## depgroupcurrent:posE.pm
## 2.7387
random effects (intercept SD and variance)
## $D
## (Intercept) posE.pmc negA_lag1.pmc
## (Intercept) 0.36338 -0.022996 -0.040259
## posE.pmc -0.02300 0.031406 -0.004679
## negA_lag1.pmc -0.04026 -0.004679 0.023684
## attr(,"L")
## [1] 0.60281 -0.03815 -0.06679 0.17306 -0.04176 0.13221
likelihood ratio test
anova(NA_posE_twopart_2, NA_posE_twopart_3)
##
## AIC BIC log.Lik LRT df p.value
## NA_posE_twopart_2 40879.76 40972.07 -20415.88
## NA_posE_twopart_3 40783.86 40887.72 -20364.93 101.89 3 <0.0001
Adding random intercept in zero-inflated model (final two-part models)
Negative events
NA_negE_twopart_4 <- mixed_model(fixed=negA ~ negE.pmc + negA_lag1.pmc + depgroup + negE.pm + depgroup*negE.pmc + depgroup*negE.pm, random=~1 + negE.pmc + negA_lag1.pmc|pident,
data = emaD,
zi_fixed = ~ negE.pmc + negA_lag1.pmc + depgroup + negE.pm + depgroup*negE.pmc + depgroup*negE.pm,
zi_random= ~ 1|pident,
family = hurdle.lognormal(), n_phis=1, control=(iter_EM = 0))
model overview
## $logLik
## 'log Lik.' -16566.49 (df=31)
##
## $AIC
## [1] 33194.98
##
## $BIC
## [1] 33314.22
##
## $call
## mixed_model(fixed = negA ~ negE.pmc + negA_lag1.pmc + depgroup +
## negE.pm + depgroup * negE.pmc + depgroup * negE.pm, random = ~1 +
## negE.pmc + negA_lag1.pmc | pident, data = emaD, family = hurdle.lognormal(),
## zi_fixed = ~negE.pmc + negA_lag1.pmc + depgroup + negE.pm +
## depgroup * negE.pmc + depgroup * negE.pm, zi_random = ~1 |
## pident, n_phis = 1, control = (iter_EM = 0))
##
## $N
## [1] 16845
##
## $phis_table
## Estimate Std.Err
## phi_1 -0.5303593 0.007147102
##
## $family
##
## Family: two-part log-normal
## Link function: identity
coefficients for the continuous part
## Estimate Std.Err z-value p-value
## (Intercept) -1.3138 0.0937 -14.0250 0.0000
## negE.pmc 0.4260 0.0522 8.1582 0.0000
## negA_lag1.pmc 0.3742 0.0175 21.3855 0.0000
## depgroupremitted 0.4877 0.1155 4.2231 0.0000
## depgroupcurrent 1.3584 0.1432 9.4867 0.0000
## negE.pm 0.8280 0.6840 1.2106 0.2261
## negE.pmc:depgroupremitted 0.0223 0.0601 0.3707 0.7109
## negE.pmc:depgroupcurrent 0.0184 0.0731 0.2519 0.8011
## depgroupremitted:negE.pm -0.1958 0.7754 -0.2525 0.8007
## depgroupcurrent:negE.pm -1.5821 0.8749 -1.8083 0.0706
coefficients for the zero-inflated part
## Estimate Std.Err z-value p-value
## (Intercept) 0.7767 0.3904 1.9897 0.0466
## negE.pmc -1.5671 0.1398 -11.2079 0.0000
## negA_lag1.pmc -1.3809 0.0691 -19.9836 0.0000
## depgroupremitted -1.7747 0.4915 -3.6108 0.0003
## depgroupcurrent -5.2901 0.6542 -8.0860 0.0000
## negE.pm -5.0347 2.9727 -1.6937 0.0903
## negE.pmc:depgroupremitted 0.0241 0.1767 0.1364 0.8915
## negE.pmc:depgroupcurrent 0.4184 0.3040 1.3763 0.1687
## depgroupremitted:negE.pm 0.4224 3.3999 0.1242 0.9011
## depgroupcurrent:negE.pm 6.4276 3.9139 1.6423 0.1005
exponential of coefficients for the zero-inflated part
## (Intercept) negE.pmc negA_lag1.pmc
## 2.1744 0.2087 0.2514
## depgroupremitted depgroupcurrent negE.pm
## 0.1695 0.0050 0.0065
## negE.pmc:depgroupremitted negE.pmc:depgroupcurrent depgroupremitted:negE.pm
## 1.0244 1.5196 1.5257
## depgroupcurrent:negE.pm
## 618.7106
random effects (intercept and slope (co)variance)
## $D
## (Intercept) negE.pmc negA_lag1.pmc zi_(Intercept)
## (Intercept) 0.39007 -0.041842 -0.050132 -1.34893
## negE.pmc -0.04184 0.061003 0.004779 0.14951
## negA_lag1.pmc -0.05013 0.004779 0.029016 0.07761
## zi_(Intercept) -1.34893 0.149514 0.077612 6.87190
## attr(,"L")
## [1] 0.624555 -0.066995 -0.080268 -2.159830 0.237729 -0.002519 0.020264
## [8] 0.150223 -0.637065 1.341928
DHARMa residual plots
DHARMa_simres <- simulateResiduals(fittedModel = NA_negE_twopart_4)
par(mfrow=c(1,2))
plotQQunif(DHARMa_simres, testUniformity = F, testOutliers = F, testDispersion = F)
plotResiduals(DHARMa_simres, rank = T, smoothScatter = F)

likelihood ratio test
anova(NA_negE_twopart_3, NA_negE_twopart_4)
##
## AIC BIC log.Lik LRT df p.value
## NA_negE_twopart_3 39951.26 40055.11 -19948.63
## NA_negE_twopart_4 33194.98 33314.22 -16566.49 6764.28 4 <0.0001
Positive events
NA_posE_twopart_4 <- mixed_model(fixed=negA ~ posE.pmc + negA_lag1.pmc + depgroup + posE.pm + depgroup*posE.pmc + depgroup*posE.pm, random=~1 + posE.pmc + negA_lag1.pmc |pident,
data = emaD,
zi_fixed = ~ posE.pmc + negA_lag1.pmc + depgroup + posE.pm + depgroup*posE.pmc + depgroup*posE.pm,
zi_random= ~ 1|pident,
family = hurdle.lognormal(), n_phis=1, control=(iter_EM = 0))
model overview
## $logLik
## 'log Lik.' -16885.42 (df=31)
##
## $AIC
## [1] 33832.84
##
## $BIC
## [1] 33952.08
##
## $call
## mixed_model(fixed = negA ~ posE.pmc + negA_lag1.pmc + depgroup +
## posE.pm + depgroup * posE.pmc + depgroup * posE.pm, random = ~1 +
## posE.pmc + negA_lag1.pmc | pident, data = emaD, family = hurdle.lognormal(),
## zi_fixed = ~posE.pmc + negA_lag1.pmc + depgroup + posE.pm +
## depgroup * posE.pmc + depgroup * posE.pm, zi_random = ~1 |
## pident, n_phis = 1, control = (iter_EM = 0))
##
## $N
## [1] 16845
##
## $phis_table
## Estimate Std.Err
## phi_1 -0.508605 0.007140645
##
## $family
##
## Family: two-part log-normal
## Link function: identity
coefficients for the continuous part
## Estimate Std.Err z-value p-value
## (Intercept) -1.0441 0.1087 -9.6009 0.0000
## posE.pmc -0.0838 0.0447 -1.8737 0.0610
## negA_lag1.pmc 0.3591 0.0172 20.8995 0.0000
## depgroupremitted 0.4340 0.1355 3.2031 0.0014
## depgroupcurrent 1.1489 0.1654 6.9469 0.0000
## posE.pm -0.4437 0.2558 -1.7347 0.0828
## posE.pmc:depgroupremitted -0.1048 0.0497 -2.1110 0.0348
## posE.pmc:depgroupcurrent -0.1006 0.0593 -1.6953 0.0900
## depgroupremitted:posE.pm 0.1327 0.3240 0.4095 0.6822
## depgroupcurrent:posE.pm -0.1260 0.4355 -0.2892 0.7724
coefficients for the zero-inflated part
## Estimate Std.Err z-value p-value
## (Intercept) -0.0589 0.4607 -0.1279 0.8982
## posE.pmc 0.7913 0.0991 7.9853 0.0000
## negA_lag1.pmc -1.3551 0.0686 -19.7651 0.0000
## depgroupremitted -1.7665 0.5886 -3.0015 0.0027
## depgroupcurrent -4.9101 0.7773 -6.3167 0.0000
## posE.pm 0.9349 1.0770 0.8680 0.3854
## posE.pmc:depgroupremitted -0.1073 0.1216 -0.8830 0.3772
## posE.pmc:depgroupcurrent -0.1438 0.1971 -0.7294 0.4658
## depgroupremitted:posE.pm -0.3205 1.4039 -0.2283 0.8194
## depgroupcurrent:posE.pm 1.2852 2.0305 0.6330 0.5268
exponential of coefficients for the zero-inflated part
## (Intercept) posE.pmc negA_lag1.pmc
## 0.9428 2.2063 0.2579
## depgroupremitted depgroupcurrent posE.pm
## 0.1709 0.0074 2.5468
## posE.pmc:depgroupremitted posE.pmc:depgroupcurrent depgroupremitted:posE.pm
## 0.8982 0.8661 0.7258
## depgroupcurrent:posE.pm
## 3.6155
random effects (intercept SD and variance)
## $D
## (Intercept) posE.pmc negA_lag1.pmc zi_(Intercept)
## (Intercept) 0.37449 -0.026318 -0.043154 -1.30702
## posE.pmc -0.02632 0.034081 -0.005129 0.11047
## negA_lag1.pmc -0.04315 -0.005129 0.026389 0.03966
## zi_(Intercept) -1.30702 0.110468 0.039659 6.92841
## attr(,"L")
## [1] 0.61196 -0.04301 -0.07052 -2.13581 0.17953 -0.04546 0.10367 0.13910
## [9] -0.76377 1.33140
DHARMa residual plots
DHARMa_simres <- simulateResiduals(fittedModel = NA_posE_twopart_4)
par(mfrow=c(1,2))
plotQQunif(DHARMa_simres, testUniformity = F, testOutliers = F, testDispersion = F)
plotResiduals(DHARMa_simres, rank = T, smoothScatter = F)

likelihood ratio test
anova(NA_posE_twopart_3, NA_posE_twopart_4)
##
## AIC BIC log.Lik LRT df p.value
## NA_posE_twopart_3 40783.86 40887.72 -20364.93
## NA_posE_twopart_4 33832.84 33952.08 -16885.42 6959.02 4 <0.0001
Final two-part models with current group as reference group
Negative events
NA_negE_twopart_4.2 <- mixed_model(fixed=negA ~ negE.pmc + negA_lag1.pmc + depgroup2 + negE.pm + depgroup2*negE.pmc + depgroup2*negE.pm, random=~1 + negE.pmc + negA_lag1.pmc|pident,
data = emaD,
zi_fixed = ~ negE.pmc + negA_lag1.pmc + depgroup2 + negE.pm + depgroup2*negE.pmc + depgroup2*negE.pm,
zi_random= ~ 1|pident,
family = hurdle.lognormal(), n_phis=1, control=(iter_EM = 0))
model overview
## $logLik
## 'log Lik.' -16567.54 (df=31)
##
## $AIC
## [1] 33197.08
##
## $BIC
## [1] 33316.32
##
## $call
## mixed_model(fixed = negA ~ negE.pmc + negA_lag1.pmc + depgroup2 +
## negE.pm + depgroup2 * negE.pmc + depgroup2 * negE.pm, random = ~1 +
## negE.pmc + negA_lag1.pmc | pident, data = emaD, family = hurdle.lognormal(),
## zi_fixed = ~negE.pmc + negA_lag1.pmc + depgroup2 + negE.pm +
## depgroup2 * negE.pmc + depgroup2 * negE.pm, zi_random = ~1 |
## pident, n_phis = 1, control = (iter_EM = 0))
##
## $N
## [1] 16845
##
## $phis_table
## Estimate Std.Err
## phi_1 -0.5303653 0.007147631
##
## $family
##
## Family: two-part log-normal
## Link function: identity
coefficients for the continuous part
## Estimate Std.Err z-value p-value
## (Intercept) -0.0216 0.1086 -0.1994 0.8420
## negE.pmc 0.4498 0.0514 8.7524 0.0000
## negA_lag1.pmc 0.3745 0.0177 21.1883 0.0000
## depgroup2remitted -0.8006 0.1266 -6.3252 0.0000
## depgroup2control -1.3039 0.1426 -9.1460 0.0000
## negE.pm -0.7531 0.5362 -1.4046 0.1601
## negE.pmc:depgroup2remitted -0.0033 0.0595 -0.0560 0.9553
## negE.pmc:depgroup2control -0.0263 0.0730 -0.3599 0.7190
## depgroup2remitted:negE.pm 1.3743 0.6379 2.1544 0.0312
## depgroup2control:negE.pm 1.5715 0.8699 1.8065 0.0708
coefficients for the zero-inflated part
## Estimate Std.Err z-value p-value
## (Intercept) -4.0080 0.5119 -7.8294 0.0000
## negE.pmc -1.1230 0.2678 -4.1937 0.0000
## negA_lag1.pmc -1.3744 0.0689 -19.9578 0.0000
## depgroup2remitted 3.0334 0.5850 5.1854 0.0000
## depgroup2control 4.8819 0.6392 7.6379 0.0000
## negE.pm 1.1235 2.4740 0.4541 0.6498
## negE.pmc:depgroup2remitted -0.4221 0.2887 -1.4619 0.1438
## negE.pmc:depgroup2control -0.4420 0.3020 -1.4638 0.1432
## depgroup2remitted:negE.pm -5.8251 2.9191 -1.9955 0.0460
## depgroup2control:negE.pm -6.3151 3.8410 -1.6441 0.1001
exponential of coefficients for the zero-inflated part
## (Intercept) negE.pmc
## 0.0182 0.3253
## negA_lag1.pmc depgroup2remitted
## 0.2530 20.7675
## depgroup2control negE.pm
## 131.8859 3.0755
## negE.pmc:depgroup2remitted negE.pmc:depgroup2control
## 0.6557 0.6427
## depgroup2remitted:negE.pm depgroup2control:negE.pm
## 0.0030 0.0018
random effects (intercept and slope (co)variance)
## $D
## (Intercept) negE.pmc negA_lag1.pmc zi_(Intercept)
## (Intercept) 0.38711 -0.041707 -0.050712 -1.29886
## negE.pmc -0.04171 0.061406 0.005555 0.12372
## negA_lag1.pmc -0.05071 0.005555 0.028157 0.07061
## zi_(Intercept) -1.29886 0.123719 0.070613 6.57204
## attr(,"L")
## [1] 0.6221785 -0.0670342 -0.0815066 -2.0876014 0.2385630 0.0003817
## [7] -0.0679958 0.1466755 -0.6784663 1.3225062
Positive events
NA_posE_twopart_4.2 <- mixed_model(fixed=negA ~ posE.pmc + negA_lag1.pmc + depgroup2 + posE.pm + depgroup2*posE.pmc + depgroup2*posE.pm, random=~1 + posE.pmc + negA_lag1.pmc |pident,
data = emaD,
zi_fixed = ~ posE.pmc + negA_lag1.pmc + depgroup2 + posE.pm + depgroup2*posE.pmc + depgroup2*posE.pm,
zi_random= ~ 1|pident,
family = hurdle.lognormal(), n_phis=1, control=(iter_EM = 0))
model overview
## $logLik
## 'log Lik.' -16885.5 (df=31)
##
## $AIC
## [1] 33832.99
##
## $BIC
## [1] 33952.23
##
## $call
## mixed_model(fixed = negA ~ posE.pmc + negA_lag1.pmc + depgroup2 +
## posE.pm + depgroup2 * posE.pmc + depgroup2 * posE.pm, random = ~1 +
## posE.pmc + negA_lag1.pmc | pident, data = emaD, family = hurdle.lognormal(),
## zi_fixed = ~posE.pmc + negA_lag1.pmc + depgroup2 + posE.pm +
## depgroup2 * posE.pmc + depgroup2 * posE.pm, zi_random = ~1 |
## pident, n_phis = 1, control = (iter_EM = 0))
##
## $N
## [1] 16845
##
## $phis_table
## Estimate Std.Err
## phi_1 -0.5086112 0.007141146
##
## $family
##
## Family: two-part log-normal
## Link function: identity
coefficients for the continuous part
## Estimate Std.Err z-value p-value
## (Intercept) 0.0823 0.1259 0.6535 0.5134
## posE.pmc -0.1832 0.0395 -4.6423 0.0000
## negA_lag1.pmc 0.3592 0.0173 20.7397 0.0000
## depgroup2remitted -0.6869 0.1494 -4.5975 0.0000
## depgroup2control -1.1407 0.1658 -6.8793 0.0000
## posE.pm -0.5586 0.3540 -1.5780 0.1146
## posE.pmc:depgroup2remitted -0.0057 0.0465 -0.1225 0.9025
## posE.pmc:depgroup2control 0.0987 0.0591 1.6700 0.0949
## depgroup2remitted:posE.pm 0.2345 0.4067 0.5766 0.5642
## depgroup2control:posE.pm 0.1332 0.4366 0.3051 0.7603
coefficients for the zero-inflated part
## Estimate Std.Err z-value p-value
## (Intercept) -4.8028 0.6284 -7.6432 0.0000
## posE.pmc 0.6476 0.1702 3.8059 0.0001
## negA_lag1.pmc -1.3544 0.0685 -19.7631 0.0000
## depgroup2remitted 2.9498 0.7252 4.0675 0.0000
## depgroup2control 4.8292 0.7782 6.2054 0.0000
## posE.pm 2.1105 1.7260 1.2228 0.2214
## posE.pmc:depgroup2remitted 0.0335 0.1842 0.1820 0.8556
## posE.pmc:depgroup2control 0.1571 0.1969 0.7976 0.4251
## depgroup2remitted:posE.pm -1.4441 1.9488 -0.7410 0.4587
## depgroup2control:posE.pm -1.2708 2.0351 -0.6244 0.5323
exponential of coefficients for the zero-inflated part
## (Intercept) posE.pmc
## 0.0082 1.9110
## negA_lag1.pmc depgroup2remitted
## 0.2581 19.1025
## depgroup2control posE.pm
## 125.1106 8.2523
## posE.pmc:depgroup2remitted posE.pmc:depgroup2control
## 1.0341 1.1701
## depgroup2remitted:posE.pm depgroup2control:posE.pm
## 0.2360 0.2806
random effects (intercept SD and variance)
## $D
## (Intercept) posE.pmc negA_lag1.pmc zi_(Intercept)
## (Intercept) 0.37696 -0.026577 -0.043567 -1.31759
## posE.pmc -0.02658 0.033652 -0.004913 0.10956
## negA_lag1.pmc -0.04357 -0.004913 0.026815 0.03747
## zi_(Intercept) -1.31759 0.109555 0.037473 6.97733
## attr(,"L")
## [1] 0.61397 -0.04329 -0.07096 -2.14602 0.17827 -0.04479 0.09346 0.14062
## [9] -0.78667 1.32074
Analysis of PA-reactivity to positive events including momentart event * event load interaction
Model_PA_posE_int <- lmer(posA ~ posE.pmc + posA_lag1.pmc + depgroup + posE.pm + posE.pmc*posE.pm + depgroup*posE.pmc + depgroup*posE.pm + (posE.pmc + posA_lag1.pmc|pident), data = emaD)
summary(Model_PA_posE_int)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: posA ~ posE.pmc + posA_lag1.pmc + depgroup + posE.pm + posE.pmc *
## posE.pm + depgroup * posE.pmc + depgroup * posE.pm + (posE.pmc +
## posA_lag1.pmc | pident)
## Data: emaD
##
## REML criterion at convergence: 33839
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -6.0687 -0.4795 0.0927 0.5770 3.8208
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## pident (Intercept) 0.48416 0.6958
## posE.pmc 0.04904 0.2215 -0.44
## posA_lag1.pmc 0.01906 0.1381 -0.05 -0.14
## Residual 0.39013 0.6246
## Number of obs: 16845, groups: pident, 346
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 4.33538 0.11943 356.08898 36.301 < 2e-16 ***
## posE.pmc 0.16503 0.04537 386.07813 3.637 0.000313 ***
## posA_lag1.pmc 0.31461 0.01074 343.01933 29.301 < 2e-16 ***
## depgroupremitted -0.67544 0.14908 358.24198 -4.531 8.02e-06 ***
## depgroupcurrent -1.57544 0.18942 358.35632 -8.317 1.90e-15 ***
## posE.pm -0.01255 0.27615 357.97578 -0.045 0.963775
## posE.pmc:posE.pm 0.20320 0.08205 401.65999 2.476 0.013680 *
## posE.pmc:depgroupremitted 0.12571 0.04023 318.82279 3.124 0.001946 **
## posE.pmc:depgroupcurrent 0.20301 0.05319 325.93909 3.817 0.000162 ***
## depgroupremitted:posE.pm 0.27331 0.34834 351.41300 0.785 0.433219
## depgroupcurrent:posE.pm 0.99667 0.49618 351.93253 2.009 0.045334 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) psE.pmc psA_1. dpgrpr dpgrpc posE.pm pE.:E.
## posE.pmc -0.196
## psA_lg1.pmc -0.008 -0.033
## depgrprmttd -0.787 0.081 -0.001
## depgrpcrrnt -0.622 0.078 -0.001 0.497
## posE.pm -0.800 0.079 0.001 0.623 0.494
## psE.pmc:pE. 0.128 -0.682 -0.010 0.009 -0.013 -0.161
## psE.pmc:dpgrpr 0.124 -0.604 -0.005 -0.147 -0.078 0.034 0.006
## psE.pmc:dpgrpc 0.108 -0.530 -0.017 -0.074 -0.158 0.008 0.112
## dpgrprmt:E. 0.617 0.032 -0.001 -0.808 -0.390 -0.770 -0.012
## dpgrpcrr:E. 0.434 0.018 -0.001 -0.348 -0.791 -0.542 -0.002
## psE.pmc:dpgrpr psE.pmc:dpgrpc dpgrpr:E.
## posE.pmc
## psA_lg1.pmc
## depgrprmttd
## depgrpcrrnt
## posE.pm
## psE.pmc:pE.
## psE.pmc:dpgrpr
## psE.pmc:dpgrpc 0.513
## dpgrprmt:E. -0.044 -0.022
## dpgrpcrr:E. -0.019 -0.037 0.430
DHARMa residual plots
DHARMa_simres <- simulateResiduals(fittedModel = Model_PA_posE_int)
par(mfrow=c(1,2))
plotQQunif(DHARMa_simres, testUniformity = F, testOutliers = F, testDispersion = F)
plotResiduals(DHARMa_simres, rank = T, smoothScatter = F)

R session info
## R version 4.0.3 (2020-10-10)
## Platform: x86_64-apple-darwin17.0 (64-bit)
## Running under: macOS Catalina 10.15.7
##
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRblas.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRlapack.dylib
##
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] GLMMadaptive_0.8-0 DHARMa_0.4.3 effects_4.2-0 car_3.0-10
## [5] carData_3.0-4 stringr_1.4.0 reshape2_1.4.4 lmerTest_3.1-3
## [9] lme4_1.1-25 Matrix_1.2-18 FSA_0.8.30 ggpubr_0.4.0
## [13] dplyr_1.0.2 knitr_1.33 moments_0.14 ggplot2_3.3.2
## [17] plyr_1.8.6
##
## loaded via a namespace (and not attached):
## [1] tidyr_1.1.2 splines_4.0.3 foreach_1.5.1
## [4] gap_1.2.2 statmod_1.4.35 highr_0.8
## [7] cellranger_1.1.0 yaml_2.2.1 numDeriv_2016.8-1.1
## [10] pillar_1.4.6 backports_1.2.0 lattice_0.20-41
## [13] glue_1.4.2 digest_0.6.27 ggsignif_0.6.0
## [16] minqa_1.2.4 colorspace_1.4-1 htmltools_0.5.0
## [19] survey_4.0 pkgconfig_2.0.3 broom_0.7.2
## [22] haven_2.3.1 purrr_0.3.4 scales_1.1.1
## [25] openxlsx_4.2.3 rio_0.5.16 tibble_3.0.4
## [28] generics_0.1.0 ellipsis_0.3.1 withr_2.3.0
## [31] nnet_7.3-14 survival_3.2-7 magrittr_1.5
## [34] crayon_1.3.4 readxl_1.3.1 evaluate_0.14
## [37] nlme_3.1-149 MASS_7.3-53 rstatix_0.6.0
## [40] forcats_0.5.0 foreign_0.8-80 tools_4.0.3
## [43] data.table_1.13.2 hms_0.5.3 mitools_2.4
## [46] matrixStats_0.57.0 lifecycle_0.2.0 munsell_0.5.0
## [49] zip_2.1.1 compiler_4.0.3 rlang_0.4.8
## [52] grid_4.0.3 nloptr_1.2.2.2 iterators_1.0.13
## [55] rmarkdown_2.10 boot_1.3-25 gtable_0.3.0
## [58] codetools_0.2-16 abind_1.4-5 DBI_1.1.0
## [61] curl_4.3 R6_2.5.0 insight_0.10.0
## [64] stringi_1.5.3 parallel_4.0.3 Rcpp_1.0.5
## [67] vctrs_0.3.4 tidyselect_1.1.0 xfun_0.25